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a total of 513 consecutive patients who were referred to the Second Affiliated Hospital of Chongqing Medical University for treatment of RVOT VPBs were © 2014 American Heart Association, Inc. Original ArticleBackground-The purpose of this study was to compare the efficacy of radiofrequency catheter ablation (RFCA) versus antiarrhythmic drugs (AADs) for treatment of patients with frequent ventricular premature beats (VPBs) originating from the right ventricular outflow tract (RVOT). Methods and Results-A total of 330 eligible patients were included in the study and were randomly assigned to RFCA or AADs group. The absolute number and the burden of VPBs on 12-lead Holter monitors were measured at baseline and at 1st, 3rd, 6th, and 12th months after randomization. Left ventricular eject fraction was evaluated by transthoracic echocardiogram at baseline and at 3 and 6 months after randomization.

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The application of optical coherence tomography (OCT) in the field of oncology has been prospering over the past decade. OCT imaging has been used to image a broad spectrum of malignancies, including those arising in the breast, brain, bladder, the gastrointestinal, respiratory, and reproductive tracts, the skin, and oral cavity, among others. OCT imaging has initially been applied for guiding biopsies, for intraoperatively evaluating tumor margins and lymph nodes, and for the early detection of small lesions that would often not be visible on gross examination, tasks that align well with the clinical emphasis on early detection and intervention. Recently, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This paper reviews the evolution of OCT technologies for the clinical application of OCT in surgical and noninvasive interventional oncology procedures and concludes with a discussion of the future directions for OCT technologies, with particular emphasis on their applications in oncology.

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Letters on IEEE Xplore. Geospatial object detection of remote sensing imagery has been attracting an increasing interest in recent years, due to the rapid development in spaceborne imaging. Most of previously proposed object detectors are very sensitive to object deformations, such as scaling and rotation. To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB). A Fourier-based rotation-invariant feature is first generated in polar coordinate. Then, the extracted features can be further structurally refined using aggregate channel features. This leads to a faster feature computation and more robust feature representation, which is good fitting for the coming boosting learning. Finally, in the test phase, we achieve a fast pyramid feature extraction by estimating a scale factor instead of directly collecting all features from image pyramid. Extensive experiments are conducted on two subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness of the FRIFB compared to previous state-of-the-art methods.

Optical coherence tomography (OCT) has become an important imaging modality with numerous biomedical applications. Challenges in high-speed, high-resolution, volumetric OCT imaging include managing dispersion, the trade-off between transverse resolution and depth-of-field, and correcting optical aberrations that are present in both the system and sample. Physics-based computational imaging techniques have proven to provide solutions to these limitations. This review aims to outline these computational imaging techniques within a general mathematical framework, summarize the historical progress, highlight the state-of-the-art achievements, and discuss the present challenges. Swanson, "Optical biopsy and imaging using optical coherence tomography," Nat. Med. 1(9), 970-972 (1995). 3. G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, "In vivo endoscopic optical biopsy with optical coherence tomography," Science 276(5321), 2037-2039 (1997). 4. S. A. Boppart, B. E. Bouma, C. Pitris, J. F. Southern, M. E. Brezinski, and J. G. Fujimoto, "In vivo cellular optical coherence tomography imaging," Nat. Med. 4(7), 861-865 (1998). Sundaram, P. S. Ray, and S. A. Boppart, "Real-time imaging of the resection bed using a handheld probe to reduce incidence of microscopic positive margins in cancer surgery," Cancer Res. 75(18), 3706-3712 (2015). 9. J. G. Fujimoto and E. A. Swanson, "The development, commercialization, and impact of optical coherence tomography," Invest. Ophthalmol. Vis. Sci. 57(9), OCT1-OCT13 (2016). 10. A. M. Cormack, "Representation of a function by its line integrals, with some radiological applications. II," J.Appl. Phys. 35(10), 2722-2727 (1964). 11. G. N. Hounsfield, "Computerized transverse axial scanning (tomography). 1. Description of system," Br. J.Radiol. 46(552), 1016-1022 (1973). 12. P. C. Lauterbur, "Image formation by induced local interactions: examples employing nuclear magnetic resonance," Nature 242(5394), 190-191 (1973). 13. P. T. Gough and D. W. Hawkins, "Unified framework for modern synthetic aperture imaging algorithms," Int. J.Imaging Syst. Technol. 8(4), 343-358 (1997). shaping for optimal depth-selective focusing in optical coherence tomography," Opt. Express 21(3), 2890-2902 (2013 111-115 (1962). 77. L. Cutrona, E. Leith, C. Palermo, and L. Porcello, "Optical data processing and filtering systems," IRE Trans.Inf. Theory 6(3), 386-400 (1960). 78. L. J. Cutrona, E. N. Leith, L. J. Porcello, and E. W. Vivian, "On the application of coherent optical processing techniques to synthetic-aperture radar," Proc. IEEE 54(8), 1026-1032 (1966). 79. W. Brown and L. Porcello, "An introduction to synthetic-aperture radar," IEEE Spectr. 6(9), 52-62 (1969). 80. M. P. Hayes and P. T. Gough, "Broad-band synthetic aperture sonar," IEEE J. Oceanic Eng. 17(1), 80-94 (1992)

Driving behavior has a large impact on vehicle fuel consumption. Dedicated study on the relationship between the driving behavior and fuel consumption can contribute to decreasing the energy cost of transportation and the development of the behavior assessment technology for the ADAS system. Therefore, it is vital to evaluate this relationship in order to develop more ecological driving assistance systems and improve the vehicle fuel economy. However, modeling driving behavior under the dynamic driving conditions is complex, making a quantitative analysis of the relationship between the driving behavior and the fuel consumption difficult. In this paper, we introduce two kinds of machine learning methods for evaluating the fuel efficiency of driving behavior using the naturalistic driving data. In the first stage, we use an unsupervised spectral clustering algorithm to study the macroscopic relationship between driving behavior and fuel consumption, using the data collected during the natural driving process. In the second stage, the dynamic information from the driving environment and natural driving data is integrated to generate a model of the relationship between various driving behaviors and the corresponding fuel consumption features. The dynamic environment factors are coded into a processable, digital form using a deep learningbased object detection method so that the environmental data can be linked with the vehicle's operating signal data to provide the training data for the deep learning network. The training data are labeled according to its fuel consumption feature distribution, which is obtained from the road segment data and historical driving data. This deep learning-based model can then be used as a predictor of the fuel consumption associated with different driving behaviors. Our results show that the proposed method can effectively identify the relationship between the driving behavior and the fuel consumption on both macro and micro levels, allowing for end-to-end fuel consumption feature prediction, which can then be applied in the advanced driving assistance systems. INDEX TERMS Driving behavior modeling, data mining, deep learning, vehicle fuel economy.